US5696884A
Expired / Lapsed
US
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Method for assisting in rendering a decision using improved belief networks
Assignee: Microsoft Technology Licensing LLC
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Filing: 1996-10-30
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Granted: 1997-12-09
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Expires: 2016-10-30
Abstract
Abstract An improved belief network generator is provided. A belief network is generated utilizing expert knowledge retrieved from an expert in a given field of expertise and empirical data reflecting observations made in the given field of the expert. In addition to utilizing expert knowledge and empirical data, the belief network generator of the preferred embodiment provides for the use of continuous variables in the generated belief network and missing data in the empirical data.
Inventors
- 👤 David E. Heckerman
- 👤 Dan Geiger
- 👤 David M. Chickering
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-02 15:55
CPC Classifications
G; G06; G06N; G06N5/00; G06N5/02; G06N5/022
Claims Analysis
Total Claims: 5
Claim 1
Independent Claim
175 words
A method in a computer system having a belief network for assisting in rendering a decision, comprising the steps of: determining whether the belief network is a complete belief network containing expert knowledge comprising variables with values that causally influence the decision, arcs indicating relationships between the variables and probabilities for each variable indicating strengths of the relationships between the variables, or an incomplete belief network having the variables and lacking the arcs; receiving empirical data, the empirical data comprising cases of observed values for the variables; when the belief network is an incomplete belief network, calculating weights for each pair of variables in the belief network, each weight having a value: determining a maximum weight spanning tree from the belief network such that a sum of the values of the calculated weights in the maximum weight spanning tree is a maximum value; and modifying the maximum weight spanning tree utilizing the received empirical data; and when the belief network is a complete belief network, modifying the complete belief network utilizing the received empirical data.
Claim 2
Independent Claim
175 words
A method in a computer system having a belief network for assisting in rendering a decision, comprising the steps of: determining whether the belief network is a complete belief network containing expert knowledge comprising variables with values that causally influence the decision, arcs indicating relationships between the variables and probabilities for each variable indicating strengths of the relationships between the variables, or an incomplete belief network having the variables and lacking the arcs; receiving empirical data, the empirical data comprising cases of observed values for the variables; when the belief network is an incomplete belief network, calculating weights for each pair of variables in the belief network, each weight having a value: determining a maximum weight spanning tree from the belief network such that a sum of the values of the calculated weights in the maximum weight spanning tree is a maximum value; and modifying the maximum weight spanning tree utilizing the received empirical data; and when the belief network is a complete belief network, modifying the complete belief network utilizing the received empirical data.
Claim 3
Dependent Claim
130 words
The method of claim 1 wherein the step of calculating weights includes the steps of: for each pair of variables in the incomplete belief network, calculating a first subscore utilizing a first variable and a second variable in the pair of variables, the first subscore for indicating goodness of the first variable for rendering inferences when an arc connects the first variable to the second variable; calculating a second subscore utilizing the first variable and an empty set, the second subscore for indicating goodness of the first variable for rendering inferences when the first variable is unconnected to the second variable; calculating a first logarithm of the first subscore; calculating a second logarithm of the second subscore; and subtracting the second logarithm from the first logarithm to create a weight.
Claim 4
Dependent Claim
184 words
A computer-readable medium whose contents cause a computer system having a belief network for assisting in rendering a decision to modify the belief network, by performing the steps of: determining whether the belief network is a complete belief network containing expert knowledge comprising variables with values that causally influence the decision, arcs indicating relationships between the variables and probabilities for each variable indicating strengths of the relationships between the variables, or an incomplete belief network having the variables and lacking the arcs; receiving empirical data, the empirical data comprising cases of observed values for the variables; when the belief network is an incomplete belief network, calculating weights for each pair of variables in the belief network, each weight having a value; determining a maximum weight spanning tree from the belief network such that a sum of the values of the calculated weights in the maximum weight spanning tree is a maximum value; and modifying the maximum weight spanning tree utilizing the received empirical data; and when the belief network is a complete belief network, modifying the complete belief network utilizing the received empirical data.
Claim 5
Dependent Claim
184 words
A computer-readable medium whose contents cause a computer system having a belief network for assisting in rendering a decision to modify the belief network, by performing the steps of: determining whether the belief network is a complete belief network containing expert knowledge comprising variables with values that causally influence the decision, arcs indicating relationships between the variables and probabilities for each variable indicating strengths of the relationships between the variables, or an incomplete belief network having the variables and lacking the arcs; receiving empirical data, the empirical data comprising cases of observed values for the variables; when the belief network is an incomplete belief network, calculating weights for each pair of variables in the belief network, each weight having a value; determining a maximum weight spanning tree from the belief network such that a sum of the values of the calculated weights in the maximum weight spanning tree is a maximum value; and modifying the maximum weight spanning tree utilizing the received empirical data; and when the belief network is a complete belief network, modifying the complete belief network utilizing the received empirical data.
References Cited (Backward Citations)
US5488725A
Cited
US5546502A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2000-12-08 | 2001-06-09 | 2001-12-09 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2004-12-08 | 2005-06-09 | 2005-12-09 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2008-12-08 | 2009-06-09 | 2009-12-09 | $7700.00 | Window Closed / Lapsed |